How to Choose Customer Questions for a Local AI Visibility Test
Which customer questions should a local business use when testing AI search visibility?
Start with questions that represent distinct customer decisions: discovering a provider, comparing alternatives, checking service-area suitability and confirming an important requirement. Write them in the language a customer would naturally use, add a location only where a customer reasonably would, and avoid naming your business in discovery questions. Use the same final wording on every platform and at every test location. A small, varied set of stable questions is more useful for comparison than a long list that changes between runs.
Start with questions that represent distinct customer decisions: discovering a provider, comparing alternatives, checking service-area suitability and confirming an important requirement. Write them in the language a customer would naturally use, add a location only where a customer reasonably would, and avoid naming your business in discovery questions. Use the same final wording on every platform and at every test location. A small, varied set of stable questions is more useful for comparison than a long list that changes between runs.
Map the customer decisions worth testing
A defensible question set begins with customer decisions, not a list of phrases designed to force the business name into an answer. A local AI visibility check can use a business, a customer question, selected AI platforms and a defined geographic area. Map four decisions before drafting: discovery, comparison, suitability and factual confirmation.
Sources: Grid My Business Launches AI Search to Map Local Visibility Across AI Platforms | Markets Insider.
Cited research says language models may prioritise content matching local intent more than a business’s overall search presence. Choose decisions that match actual services, genuine service boundaries and customer concerns rather than every possible keyword.
Sources: ‘Near Me’ Queries & What Brands Sometimes Miss About Geographic Coverage in AI Search.
- Discovery: finding a provider without knowing a business name.
- Comparison: choosing between plausible local options.
- Suitability: checking availability, specialisation or service-area fit.
- Confirmation: checking a known business’s essential facts.
Build a balanced question bank
A balanced question bank gives each prompt one job, making later observations easier to interpret. Begin with six to ten questions that cover distinct decisions instead of collecting many near-duplicates. Replace bracketed terms with the business’s real service, customer need and relevant place names.
- Discovery pattern: “Who offers [service] near [location]?”
- Comparison pattern: “What are some [service] options in [location]?”
- Suitability pattern: “Who can help with [specific need] in [location]?”
- Requirement pattern: “Which [service] providers in [location] offer [important requirement]?”
- Branded accuracy pattern: “What services does [business name] provide?”
- Service-area pattern: “Does [business name] serve [location]?”
Separate discovery questions from branded accuracy checks
Discovery questions test whether an unfamiliar customer may encounter the business, while branded questions test whether known business facts are represented accurately. Keep the two groups in separate tabs or labels so a correct branded answer is not mistaken for discovery visibility. Use branded checks for address, service area, offering, opening details and other facts that a customer could rely on.
- Do not name the business in a discovery question.
- Do name the business when checking factual accuracy.
- Do not average discovery and branded results together.
- Record factual errors separately from absent discovery mentions.
Control wording and geographic context
Comparable question testing requires frozen wording and one consistently documented way of expressing location. Location-based result checks can show how answers vary across different parts of a business’s service area. Use the same place name, spelling and level of detail for every platform when a question requires a location.
Sources: Grid My Business Launches AI Search to Map Local Visibility Across AI Platforms | Markets Insider.
A testing workflow can specify a customer question, selected AI platforms and a geographic area. Avoid changing the service description, customer need and place name in the same comparison because the result will no longer isolate a useful difference.
Sources: Grid My Business Launches AI Search to Map Local Visibility Across AI Platforms | Markets Insider.
- Freeze final wording before the first run.
- Use one location naming convention, such as suburb and state.
- Do not add persuasive adjectives that a customer would not naturally use.
- Note whether location came from a platform setting or the question text.
- Create a new question version when a material wording change is necessary.
Review and version the final question set
A final review prevents a question bank from becoming biased, repetitive or disconnected from the customer journey. Give the set a simple version name, date and owner before using it in a visibility check. Change the version only when the service offering, service boundary or customer decision genuinely changes.
- Can a real customer plausibly ask this question?
- Does each question test a distinct decision?
- Does the wording avoid unnecessary business-name bias?
- Is the service and location genuinely supported?
- Could another person repeat the wording exactly?
- Has the version and change reason been recorded?
Intent-based local customer question bank
Use these neutral patterns as a starting point. Replace bracketed terms with genuine services and locations, then freeze wording before testing.
| Intent | What it tests | Neutral prompt pattern |
|---|---|---|
| Discovery | Whether an unfamiliar customer may encounter providers | Who offers [service] near [location]? |
| Comparison | Which local alternatives appear | What are some [service] options in [location]? |
| Suitability | Fit for a specific customer need | Who can help with [need] in [location]? |
| Requirement | A meaningful capability or constraint | Which [service] providers in [location] offer [requirement]? |
| Branded accuracy | Known business facts | Does [business name] serve [location]? |
Keep branded accuracy checks separate from non-branded discovery questions. The patterns do not reveal how any platform ranks or generates answers.
Frequently asked questions
How many questions should I include?
Start with six to ten varied questions. Add another only when it represents a distinct customer decision. A small stable set is easier to repeat and compare than a large changing bank.
Should every question include a suburb name?
No. Add a location where a customer would naturally use one. For a service-area test, use a consistent location approach across the relevant questions and record it.
Can I ask a question that names my business?
Yes, but classify it as a branded accuracy check. It can reveal incorrect business information, but it does not test whether an unfamiliar customer would discover the business.
Should I ask the exact same questions on every platform?
Yes. Keeping wording stable allows you to compare observations while still recognising that platforms may produce answers differently.
Related guidance
What follow-up questions matter most?
- How many questions should I include?
- Start with six to ten varied questions. Add another only when it represents a distinct customer decision. A small stable set is easier to repeat and compare than a large changing bank.
- Should every question include a suburb name?
- No. Add a location where a customer would naturally use one. For a service-area test, use a consistent location approach across the relevant questions and record it.
- Can I ask a question that names my business?
- Yes, but classify it as a branded accuracy check. It can reveal incorrect business information, but it does not test whether an unfamiliar customer would discover the business.
- Should I ask the exact same questions on every platform?
- Yes. Keeping wording stable allows you to compare observations while still recognising that platforms may produce answers differently.
What steps does this workflow follow?
Create a reusable local AI question bank
- List customer decisions: Write the discovery, comparison, suitability and factual-confirmation decisions that matter most to customers.
- Draft neutral patterns: Turn each decision into plain-language questions using real services and only relevant locations.
- Separate branded checks: Place business-name questions in an accuracy group rather than mixing them with discovery prompts.
- Freeze the wording: Choose final wording, spelling and location format before running the first comparison.
- Version the bank: Record the question-set name, date and any future reason for changing a prompt.